3D Diffraction Imaging and Diffraction Attributes Based on Asymmetric Summation: Real Data Application

2020 ◽  
Author(s):  
M. Protasov ◽  
V. Tcheverda ◽  
V. Shilikov ◽  
A. Ledyaev
Keyword(s):  
Real Data ◽  
Data Application ◽  
Diffraction Imaging

Diffraction images and their attributes based on asymmetric summation: real data application

2020 ◽  
Author(s):  
Maxim I. Protasov ◽  
◽  
Vladimir A. Tcheverda ◽  
Valery V. Shilikov ◽  
◽  
...  
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Data Sets ◽  
Data Application ◽  
Diffraction Imaging

The paper deals with a 3D diffraction imaging with the subsequent diffraction attribute calculation. The imaging is based on an asymmetric summation of seismic data and provides three diffraction attributes: structural diffraction attribute, point diffraction attribute, an azimuth of structural diffraction. These attributes provide differentiating fractured and cavernous objects and to determine the fractures orientations. Approbation of the approach was provided on several real data sets.

scholarly journals Slash Truncation Positive Normal Distribution and Its Estimation Based on the EM Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2164
Author(s):  
Héctor J. Gómez ◽  
Diego I. Gallardo ◽  
Karol I. Santoro
Keyword(s):  
Expectation Maximization Algorithm ◽  
Likelihood Estimation ◽  
Real Data ◽  
Simulation Studies ◽  
R Software ◽  
Data Application ◽  
The Em Algorithm ◽  
Kurtosis Parameter ◽  
Computational Implementation ◽  
Moments Method

In this paper, we present an extension of the truncated positive normal (TPN) distribution to model positive data with a high kurtosis. The new model is defined as the quotient between two random variables: the TPN distribution (numerator) and the power of a standard uniform distribution (denominator). The resulting model has greater kurtosis than the TPN distribution. We studied some properties of the distribution, such as moments, asymmetry, and kurtosis. Parameter estimation is based on the moments method, and maximum likelihood estimation uses the expectation-maximization algorithm. We performed some simulation studies to assess the recovery parameters and illustrate the model with a real data application related to body weight. The computational implementation of this work was included in the tpn package of the R software.

scholarly journals Causal variance decompositions for institutional comparisons in healthcare

2019 ◽  
Vol 29 (7) ◽  
pp. 1972-1986
Author(s):  
Bo Chen ◽  
Keith A Lawson ◽  
Antonio Finelli ◽  
Olli Saarela
Keyword(s):  
Random Effect ◽  
Real Data ◽  
Hospital Performance ◽  
Case Mix ◽  
Link Functions ◽  
Variation In Care ◽  
Residual Variation ◽  
Data Application ◽  
Variance Decompositions ◽  
Random Effect Models

There is increasing interest in comparing institutions delivering healthcare in terms of disease-specific quality indicators (QIs) that capture processes or outcomes showing variations in the care provided. Such comparisons can be framed in terms of causal models, where adjusting for patient case-mix is analogous to controlling for confounding, and exposure is being treated in a given hospital, for instance. Our goal here is to help identify good QIs rather than comparing hospitals in terms of an already chosen QI, and so we focus on the presence and magnitude of overall variation in care between the hospitals rather than the pairwise differences between any two hospitals. We consider how the observed variation in care received at patient level can be decomposed into that causally explained by the hospital performance adjusting for the case-mix, the case-mix itself, and residual variation. For this purpose, we derive a three-way variance decomposition, with particular attention to its causal interpretation in terms of potential outcome variables. We propose model-based estimators for the decomposition, accommodating different link functions and either fixed or random effect models. We evaluate their performance in a simulation study and demonstrate their use in a real data application.

Model Diagnostics for Bayesian Networks

2006 ◽  
Vol 31 (1) ◽  
pp. 1-33 ◽  
Author(s):  
Sandip Sinharay
Keyword(s):  
Model Checking ◽  
Bayesian Networks ◽  
Real Data ◽  
Diagnostic Tools ◽  
Model Diagnostics ◽  
Posterior Predictive Model Checking ◽  
Item Fit ◽  
Item Functioning ◽  
Data Application ◽  
Educational Assessments

Bayesian networks are frequently used in educational assessments primarily for learning about students’ knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A number of aspects of model fit, those of usual interest to practitioners, are assessed using various diagnostic tools. This article suggests a direct data display for assessing overall fit, suggests several diagnostics for assessing item fit, suggests a graphical approach to examine if the model can explain the association among the items, and suggests a version of the Mantel–Haenszel statistic for assessing differential item functioning. Limited simulation studies and a real data application demonstrate the effectiveness of the suggested model diagnostics.

scholarly journals Mixture of Lindley and Inverse Weibull Distributions: Properties and Estimation

2021 ◽  
Vol 20 ◽  
pp. 134-143
Author(s):  
A. S. Al-Moisheer ◽  
A. F. Daghestani ◽  
K. S. Sultan
Keyword(s):  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Real Data ◽  
Estimation Methods ◽  
Simulation Studies ◽  
Weibull Distributions ◽  
Unknown Parameters ◽  
Data Application ◽  
Rate Functions ◽  
And Behavior

In this paper, we talk about a mixture of one-parameter Lindley and inverse Weibull distributions (MLIWD). First, We introduce and discuss the MLIWD. Then, we study the main statistical properties of the proposed mixture and provide some graphs of both the density and the associated hazard rate functions. After that, we estimate the unknown parameters of the proposed mixture via two estimation methods, namely, the generalized method of moments and maximum likelihood. In addition, we compare the estimation methods via some simulation studies to determine the efficacy of the two estimation methods. Finally, we evaluate the performance and behavior of the proposed mixture with different numerical examples and real data application in survival analysis.

scholarly journals A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models

2021 ◽  
Author(s):  
Niklas Maltzahn ◽  
Rune Hoff ◽  
Odd O. Aalen ◽  
Ingrid S. Mehlum ◽  
Hein Putter ◽  
...  
Keyword(s):  
Statistical Power ◽  
Transition Probabilities ◽  
Real Data ◽  
Testing Procedure ◽  
Data Application ◽  
Stratification Method ◽  
Model Complex ◽  
Markov Assumption ◽  
State Models ◽  
Disability Education

AbstractMulti-state models are increasingly being used to model complex epidemiological and clinical outcomes over time. It is common to assume that the models are Markov, but the assumption can often be unrealistic. The Markov assumption is seldomly checked and violations can lead to biased estimation of many parameters of interest. This is a well known problem for the standard Aalen-Johansen estimator of transition probabilities and several alternative estimators, not relying on the Markov assumption, have been suggested. A particularly simple approach known as landmarking have resulted in the Landmark-Aalen-Johansen estimator. Since landmarking is a stratification method a disadvantage of landmarking is data reduction, leading to a loss of power. This is problematic for “less traveled” transitions, and undesirable when such transitions indeed exhibit Markov behaviour. Introducing the concept of partially non-Markov multi-state models, we suggest a hybrid landmark Aalen-Johansen estimator for transition probabilities. We also show how non-Markov transitions can be identified using a testing procedure. The proposed estimator is a compromise between regular Aalen-Johansen and landmark estimation, using transition specific landmarking, and can drastically improve statistical power. We show that the proposed estimator is consistent, but that the traditional variance estimator can underestimate the variance of both the hybrid and landmark estimator. Bootstrapping is therefore recommended. The methods are compared in a simulation study and in a real data application using registry data to model individual transitions for a birth cohort of 184 951 Norwegian men between states of sick leave, disability, education, work and unemployment.

scholarly journals The partly parametric and partly nonparametric additive risk model

2021 ◽  
Author(s):  
Nils Lid Hjort ◽  
Emil Aas Stoltenberg
Keyword(s):  
Hazard Rate ◽  
Goodness Of Fit ◽  
Risk Model ◽  
Real Data ◽  
Rate Function ◽  
Nonparametric Model ◽  
Additive Risk Model ◽  
Data Application ◽  
Additive Risk ◽  
Hazard Factor

AbstractAalen’s linear hazard rate regression model is a useful and increasingly popular alternative to Cox’ multiplicative hazard rate model. It postulates that an individual has hazard rate function $$h(s)=z_1\alpha _1(s)+\cdots +z_r\alpha _r(s)$$ h ( s ) = z 1 α 1 ( s ) + ⋯ + z r α r ( s ) in terms of his covariate values $$z_1,\ldots ,z_r$$ z 1 , … , z r . These are typically levels of various hazard factors, and may also be time-dependent. The hazard factor functions $$\alpha _j(s)$$ α j ( s ) are the parameters of the model and are estimated from data. This is traditionally accomplished in a fully nonparametric way. This paper develops methodology for estimating the hazard factor functions when some of them are modelled parametrically while the others are left unspecified. Large-sample results are reached inside this partly parametric, partly nonparametric framework, which also enables us to assess the goodness of fit of the model’s parametric components. In addition, these results are used to pinpoint how much precision is gained, using the parametric-nonparametric model, over the standard nonparametric method. A real-data application is included, along with a brief simulation study.

Enhancing interpretability with diffraction imaging using plane-wave destruction aided by frequency-wavenumber f-k filtering

2020 ◽  
Vol 8 (3) ◽  
pp. T541-T554
Author(s):  
Brydon Lowney ◽  
Ivan Lokmer ◽  
Gareth Shane O’Brien ◽  
Lawrence Amy ◽  
Christopher J. Bean ◽  
...  
Keyword(s):  
Plane Wave ◽  
Synthetic Data ◽  
Real Data ◽  
Fault Zones ◽  
Seismic Interpretation ◽  
Established Method ◽  
Specular Reflections ◽  
Diffraction Imaging ◽  
Sharp Corners ◽  
Imaging Plane

A conventional processing workflow favors only the specular reflections, reducing or removing other wavefield interactions. These specular reflections are unsuitable for directly imaging sharp corners, such as those in fault zones and pinch outs; therefore, diffractions are used instead in a technique known as diffraction imaging. Plane-wave destruction (PWD) is a well-established method for removing reflections and imaging diffractions. However, this method assumes a gently variable slope; therefore, it fails to remove energy in areas that do not follow this assumption such as curved interfaces. To remove the remnant energy in these areas and thus enhance the overall interpretability of the diffraction images, we have adopted a simple spatial-variable filter in the frequency-wavenumber f-k domain based on the calculated dip field used for PWD, applied post PWD. To demonstrate the method, we have examined this on a range of synthetic data, complex synthetic data, and real data. The created diffraction images have then been interpreted to evidence the benefit of diffraction imaging in seismic interpretation, helping to delineate pinch outs, faults, and rugose surfaces.

Shallow tunnel detection using SH-wave diffraction imaging

Geophysics ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. EN29-EN37 ◽  
Author(s):  
Shelby L. Peterie ◽  
Richard D. Miller ◽  
Julian Ivanov ◽  
Steven D. Sloan
Keyword(s):  
Wave Diffraction ◽  
Geophysical Methods ◽  
Real Data ◽  
High Amplitude ◽  
P Wave ◽  
Shallow Tunnel ◽  
Sh Wave ◽  
Security Threat ◽  
Shallow Tunnels ◽  
Diffraction Imaging

Clandestine tunnels, used for drug or human trafficking and tactical operations, pose a security threat worldwide and remain elusive targets for detection with geophysical methods. P-wave diffraction imaging is an increasingly common technique for detecting subsurface discontinuities that are smaller than the seismic wavelength (such as faults, pinch outs, and small voids) and has been successfully used to detect shallow tunnels. P-wave diffractions from tunnels typically have very low signal-to-noise ratios and are therefore challenging wavefield components for imaging. Mode-specific amplitude characteristics of theoretical diffractions from a shallow tunnel were evaluated using 9C seismic modeling. Results indicate that SH-wave diffraction has the largest amplitude and coherent phase characteristics along the traveltime hyperbola, making it ideal for diffraction imaging. In real data acquired over a 9.2 m deep tunnel, amplitudes of SH-wave diffractions are 20 dB greater than P-wave diffractions. The tunnel signature on the P-wave diffraction section is of low amplitude relative to the background. The SH-wave diffraction section has a high-amplitude signal focused at the horizontal location and a traveltime consistent with the tunnel location, indicating that the SH-wave may be optimal for diffraction imaging to detect shallow tunnels.

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